Insurance Underwriting business rules involve carefully assessing relevant information to determine risk levels and set appropriate terms for financial transactions or insurance policies. However, updating these rules is not as easy as expected during underwriting.
- Time-consuming: Updating business rules can be time-consuming, especially for complex or large-scale underwriting business rule sets.
- Regulatory compliance: In the process of updating the underwriting business rules, organizations must follow the rules and regulations set by the authorities. This will be challenging because regulations change over time, and understanding and implementing those changes to the existing ruleset can be critical. Organizations must stay up to date, correctly interpret the rules, and adapt to new compliance requirements.
- Testing and implementation: Implementing changes to the underwriting process requires thorough testing to ensure functionality, accuracy, and reliability. Developing comprehensive testing plans are vital to minimize disruptions and mitigate potential risks.
- Complexity: Underwriting business rules can be inherently complex, involving intricate logic, multiple conditions, and exceptions. Understanding the intricacies of existing rules and making changes without introducing errors or unintended consequences requires careful analysis and validation.
Due to these challenges, updating business rulesets in the insurance underwriting process can be biased, inconsistent, error-prone, and involve subjective judgments based on the underwriter's interpretation. However, these challenges can be mitigated by approaching different ways of logic implementation to offer enhanced readability, maintainability, and comprehension. In this blog post, we discuss three different approaches that can help overcome these complexities in the underwriting process.
The scenario of an underwriting process
In an insurance company, we encounter numerous business rules such as Previous Policy Cancelled, Previous Insurance Denied, Hail Damage, car value over $70K, Previous License Cancelled, and many others. Each ruleset necessitates specific actions and updates the client portal. To address this scenario effectively, we require an approach that facilitates easy updates to rulesets while ensuring consistent and accurate results.
Three different logic implementation approaches
Here are three different approaches for this logic implementation while addressing the above-mentioned challenges.
- Standard Decision Table
- Vertical Decision Table
- Natural Language
The primary approach is a standard decision table with conditions for each business rule. By utilizing this decision table, we can easily define and understand the underwriting business rules by covering all possible combinations and having a condition for each scenario. In this Decision Table, we have defined separate business terms for each condition in a Business Glossary.
However, a challenge with this approach arises when dealing with increasing business rulesets. For a scenario implemented in the following Decision table, modifying the business rulesets becomes challenging due to the structure of the decision table.
The second approach is implementing the same logic differently in a Decision Table. In this approach, we have used a Decision Table to build the expressions vertically as rows. For example, if you have a long list of conditions on which you want to run a set of rules, this pattern can be used. In this implementation, we separate the expression building into 3 steps: data field, operator, and value. The expressions will be created during the execution.
As shown in the image, it offers enhanced readability and comprehension. Moreover, modifying business rulesets is notably more straightforward than the previous approach with the growing rule set.
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The third approach is the Natural Language approach, which offers an alternative method for formulating business rules and logic using business terminologies. This approach lets you define your common language using a business glossary and fact concepts to guide rule creation. The Natural Language approach is highly effective for expressing business rules. Its ability to generate executable and testable business rules has gained popularity. By employing our advanced Domain Specific Language, you can utilize the domain-specific language of your industry (such as insurance or healthcare) to write rules efficiently and accurately.
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Transparency and Traceability
Ensuring transparency and clear explainability in decision-making builds trust, and enhances customer satisfaction. This allows for a detailed view and informs precisely what happens behind each step of the decision-making process.
Another advantage of our approach is minimizing the likelihood of mistakes by giving the ability to create test cases for users. We can cross-check the updated business rules against the automated testcases.
Updating underwriting business rules can be problematic if the business rules related to underwriting are hard-coded inside the application, API layer, business processes and so on. The decisions related to underwriting should be updated as regularly as it needs without the need for IT professionals and software development teams in the organization if they are designed appropriately. These decisions are generally rule-driven, and as discussed in this blog post, there are multiple options to model these rules.
The benefit of using our approach is that it empowers non-technical members of organizations i.e. operations and SEMs and underwriting teams, to not only manage the business rules around underwriting but also test, debug, simulate, and deploy them as needed without any delay and wait for IT development team. They can confidently test and simulate and securely deploy the decisions. And more importantly, the results are fully transparent i.e. explainable, auditable, and tracible.
Last updated June 5th, 2023 at 05:03 pm, Published June 1st, 2023 at 05:03 pm